Download or read book A Framework for Privacy preserving Distributed Machine Learning Using Gradient Obfuscation written by Nishad Ashok Phadke and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust and Privacy Preserving Distributed Machine Learning written by Rania Talbi and published by . This book was released on 2021 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the pervasiveness of digital services, huge amounts of data are nowadays continuously generated and collected. Machine Learning (ML) algorithms allow the extraction of hidden yet valuable knowledge from these data and have been applied in numerous domains, such as health care assistance, transportation, user behavior prediction, and many others. In many of these applications, data is collected from different sources and distributed training is required to learn global models over them. However, in the case of sensitive data, running traditional ML algorithms over them can lead to serious privacy breaches by leaking sensitive information about data owners and data users. In this thesis, we propose mechanisms allowing to enhance privacy preservation and robustness in the domain of distributed machine learning. The first contribution of this thesis falls in the category of cryptography-based privacy preserving machine learning. Many state-of-the-art works propose cryptography-based solutions to ensure privacy preservation in distributed machine learning. Nonetheless, these works are known to induce huge overheads time and space-wise. In this line of works, we propose PrivML an outsourced Homomorphic Encryption-based Privacy Preserving Collaborative Machine Learning framework, that allows optimizing runtime and bandwidth consumption for widely used ML algorithms, using many techniques such as ciphertext packing, approximate computations, and parallel computing. The other contributions of this thesis address the robustness issues in the domain of Federated Learning. Indeed federated learning is the first framework to ensure privacy by design for distributed machine learning. Nonetheless, it has been shown that this framework is still vulnerable to many attacks, among them we find poisoning attacks, where participants deliberately use faulty training data to provoke misclassification at inference time. We demonstrate that state-of-the-art poisoning mitigation mechanisms fail to detect some poisoning attacks and propose ARMOR, a poisoning mitigation mechanism for Federated Learning that successfully detects these attacks, without hurting models' utility.

Download or read book Privacy Preserving Machine Learning written by Srinivasa Rao Aravilli and published by Packt Publishing Ltd. This book was released on 2024-05-24 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain hands-on experience in data privacy and privacy-preserving machine learning with open-source ML frameworks, while exploring techniques and algorithms to protect sensitive data from privacy breaches Key Features Understand machine learning privacy risks and employ machine learning algorithms to safeguard data against breaches Develop and deploy privacy-preserving ML pipelines using open-source frameworks Gain insights into confidential computing and its role in countering memory-based data attacks Purchase of the print or Kindle book includes a free PDF eBook Book Description– In an era of evolving privacy regulations, compliance is mandatory for every enterprise – Machine learning engineers face the dual challenge of analyzing vast amounts of data for insights while protecting sensitive information – This book addresses the complexities arising from large data volumes and the scarcity of in-depth privacy-preserving machine learning expertise, and covers a comprehensive range of topics from data privacy and machine learning privacy threats to real-world privacy-preserving cases – As you progress, you’ll be guided through developing anti-money laundering solutions using federated learning and differential privacy – Dedicated sections will explore data in-memory attacks and strategies for safeguarding data and ML models – You’ll also explore the imperative nature of confidential computation and privacy-preserving machine learning benchmarks, as well as frontier research in the field – Upon completion, you’ll possess a thorough understanding of privacy-preserving machine learning, equipping them to effectively shield data from real-world threats and attacks What you will learn Study data privacy, threats, and attacks across different machine learning phases Explore Uber and Apple cases for applying differential privacy and enhancing data security Discover IID and non-IID data sets as well as data categories Use open-source tools for federated learning (FL) and explore FL algorithms and benchmarks Understand secure multiparty computation with PSI for large data Get up to speed with confidential computation and find out how it helps data in memory attacks Who this book is for – This comprehensive guide is for data scientists, machine learning engineers, and privacy engineers – Prerequisites include a working knowledge of mathematics and basic familiarity with at least one ML framework (TensorFlow, PyTorch, or scikit-learn) – Practical examples will help you elevate your expertise in privacy-preserving machine learning techniques

Download or read book Privacy Preserving Deep Learning written by Kwangjo Kim and published by Springer Nature. This book was released on 2021-07-22 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google’s infamous announcement of “Private Join and Compute,” an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world. This book provides fundamental insights into privacy-preserving and deep learning, offering a comprehensive overview of the state-of-the-art in PPDL methods. It discusses practical issues, and leveraging federated or split-learning-based PPDL. Covering the fundamental theory of PPDL, the pros and cons of current PPDL methods, and addressing the gap between theory and practice in the most recent approaches, it is a valuable reference resource for a general audience, undergraduate and graduate students, as well as practitioners interested learning about PPDL from the scratch, and researchers wanting to explore PPDL for their applications.

Download or read book Federated Learning written by Qiang Qiang Yang and published by Springer Nature. This book was released on 2022-06-01 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Download or read book Secure and Privacy Aware Machine Learning written by Xuhui Chen and published by . This book was released on 2019 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the onset of the big data era, designing efficient and secure machine learning frameworks to analyze large-scale data is in dire need. This dissertation considers two machine learning paradigms, the centralized learning scenario, where we study the secure outsourcing problem in cloud computing, and the distributed learning scenario, where we explore blockchain techniques to remove the untrusted central server to solve the security problems. In the centralized machine learning paradigm, inference using deep neural networks (DNNs) may be outsourced to the cloud due to its high computational cost, which, however, raises security concerns. Particularly, the data involved in DNNs can be highly sensitive, such as in medical, financial, commercial applications, and hence should be kept private. Besides, DNN models owned by research institutions or commercial companies are their valuable intellectual properties and can contain proprietary information, which should be protected as well. Moreover, an untrusted cloud service provider may return inaccurate and even erroneous computing results. To address above issues, we propose a secure outsourcing framework for deep neural network inference called SecureNets, which can preserve both a user's data privacy and his/her neural network model privacy, and also verify the computation results returned by the cloud. Specifically, we employ a secure matrix transformation scheme in SecureNets to avoid privacy leakage of the data and the model. Meanwhile, we propose a verification method that can efficiently verify the correctness of cloud computing results. Our simulation results on four- and five-layer deep neural networks demonstrate that SecureNets can reduce the processing runtime by up to 64%. Compared with CryptoNets, one of the previous schemes, SecureNets can increase the throughput by 104.45% while reducing the data transmission size by 69.78% per instance. We further improve the privacy level in SecureNets and implement it in a practical scenario. The Internet of Things (IoT) emerge as a ubiquitous information collection and processing paradigm that can potentially exploit the collected massive data for various applications like smart health, smart transportation, cyber-physical systems, by taking advantage of machine learning technologies. However, these data are usually unlabeled, while the labeling process is usually both time and effort consuming. Active learning is one approach to reduce the data labeling cost by only sending the most informative samples to experts for labeling. In this process, two most computation-intensive operations, i.e., sample selection and learning model training, hinder the use of active learning on resource-limited IoT devices. To address this issue, we develop a secure outsourcing framework for deep active learning (SEDAL) by considering a general active learning framework with a deep neural network (DNN) learning model. The improved SecureNets is adopted in the model inferences in sample selection and DNN learning phases. Compared with traditional homomorphic encryption based secure outsourcing schemes, our scheme reduces the computational complexity at the user from O(n^3) to O(n^2). To evaluate the performance of the proposed system, we implement it on an android phone and Amazon AWS cloud for an arrhythmia diagnosis application. Experiment results show that the proposed scheme can obtain a well-trained classifier using fewer queried samples, and the computation time and communication overhead are acceptable and practical. Besides the centralized learning paradigms, in practice, data can also be generated by multiple parties and stored in a geographically distributed manner, which spurs the study of distributed machine learning. Traditional master-worker type of distributed machine learning algorithms assumes a trusted central server and focuses on the privacy issue in linear learning models, while privacy in nonlinear learning models and security issues are not well studied. To address these issues, in this work, we explore the blockchain technique to propose a decentralized privacy-preserving and secure machine learning system, called LearningChain, by considering a general (linear or nonlinear) learning model and without a trusted central server. Specifically, we design a decentralized Stochastic Gradient Descent (SGD) algorithm to learn a general predictive model over the blockchain. In decentralized SGD, we develop differential privacy based schemes to protect each party's data privacy, and propose an l-nearest aggregation algorithm to protect the system from potential Byzantine attacks. We also conduct theoretical analysis on the privacy and security of the proposed LearningChain. Finally, we implement LearningChain and demonstrate its efficiency and effectiveness through extensive experiments.

Download or read book Privacy Preserving Machine Learning written by Jin Li and published by Springer Nature. This book was released on 2022-03-14 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datasets and not fully trusted environments, machine learning-based applications face enormous security and privacy risks. In turn, it presents studies conducted to address privacy issues and a series of proposed solutions for ensuring privacy protection in machine learning tasks involving multiple parties. In closing, the book reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.

Download or read book Towards Efficient and Effective Privacy Preserving Machine Learning written by Lingxiao Wang and published by . This book was released on 2021 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has witnessed the fast growth and tremendous success of machine learning. However, recent studies showed that existing machine learning models are vulnerable to privacy attacks, such as membership inference attacks, and thus pose severe threats to personal privacy. Therefore, one of the major challenges in machine learning is to learn effectively from enormous amounts of sensitive data without giving up on privacy. This dissertation summarizes our contributions to the field of privacy-preserving machine learning, i.e., solving machine learning problems with strong privacy and utility guarantees. In the first part of the dissertation, we consider the privacy-preserving sparse learning problem. More specifically, we establish a novel differentially private hard-thresholding method as well as a knowledge-transfer framework for solving the sparse learning problem. We show that our proposed methods are not only efficient but can also achieve improved privacy and utility guarantees. In the second part of the dissertation, we propose novel efficient and effective algorithms for solving empirical risk minimization problems. To be more specific, our proposed algorithms can reduce the computational complexities and improve the utility guarantees for solving nonconvex optimization problems such as training deep neural networks. In the last part of the dissertation, we study the privacy-preserving empirical risk minimization in the distributed setting. In such a setting, we propose a new privacy-preserving framework by combining the multi-party computation (MPC) protocol and differentially private mechanisms and show that our framework can achieve better privacy and utility guarantees compared with existing methods. The methods and techniques proposed in this dissertation form a line of researches that deepens our understandings of the trade-off between privacy, utility and efficient in privacy-preserving machine learning, and could also help us develop more efficient and effective private learning algorithms.

Download or read book Privacy Preserving Machine Learning written by Srinivasa Rao Aravilli and published by Packt Publishing. This book was released on 2023-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book helps software engineers, data scientists, ML and AI engineers, and research and development teams to learn and implement privacy-preserving machine learning as well as protect companies against privacy breaches.

Download or read book Privacy Preserving Algorithms for Machine Learning written by Shuang Song and published by . This book was released on 2018 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern machine learning increasingly involves personal data, such as healthcare, financial and user behavioral information. However, models trained on such data can reveal detailed information about the data and cause a serious privacy breach. Consequently, it is important to design algorithms that can analyze the sensitive data while still preserving privacy. This thesis advances the state-of-the-art of privacy-preserving machine learning in the following two major aspects. First, this thesis addresses the challenges in differentially private large-scale machine learning. On the one hand, with a large amount of sensitive user data, privacy-preserving learning algorithms are expected to achieve improved utility. On the other hand, big data imposes additional challenges, including performance (a.k.a Volume), data noise (a.k.a Veracity), a large number of classes and distributed sources (a.k.a Variety). This thesis presents (1) private versions of the widely used stochastic gradient descent (SGD) algorithm with generalization to data from multiple sources of different privacy requirements, and (2) an improved version of the Private Aggregation of Teacher Ensembles (PATE) framework which can scale to learning tasks with a large number of output classes and uncurated, imbalanced training data. Second, this thesis considers privacy-preserving data analysis beyond tabular data. Differential privacy is best suited for tabular data, where each record corresponds to all the information about an individual and records are independent of each other. However, many real-world applications involve non-tabular sensitive data, such as epidemic transmission graphs and measurement of the physical activity of a single subject across time. To analyze disease transmission graphs, or more general, graphs with sensitive information stored in each node, this thesis considers privacy-preserving continual release of graph statistics, such as the percentages of highly-active patients over time. The proposed algorithm outperforms the baselines in utility over a range of parameters. For physical activity measurement, or more generally, data with correlation, this thesis looks at a recent generalization of differential privacy, called Pufferfish privacy, that addresses privacy concerns in correlated data. Two mechanisms that work under different scenarios are proposed, and one of them is evaluated on real and synthetic time-series data.

Download or read book Privacy Preserving Framework for Federated Learning in Genomics written by Yashashree Kokje and published by . This book was released on 2020 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of machine learning, organizations today collect and process data at an unprecedented scale. This has led to rapid growth in innovation across industries, but also poses numerous challenges around maintaining user privacy. Specifically, in the field of healthcare and genomics where data is highly sensitive. Unlike credit cards or passwords, one’s genomic information cannot be modified at will and has the ability to uniquely identify the individual. The objective of this thesis is to develop an easily configurable framework that would allow organizations to collaborate and advance genomic research without directly sharing user data with each other. This thesis includes the development of a privacy preserving framework for federated learning on genomic datasets that are distributed across organizational silos. PAGe (Privacy Aware Genomics) has been open-sourced and has a low barrier to entry. A packaged runtime environment is available that includes popular bioinformatics tools and machine learning libraries. Experimental setup is controlled through configuration files, allowing users to easily terminate, restart or reproduce results. Finally, there is an in depth evaluation of the framework using Type 2 Diabetes disease risk prediction as a case study with the 1000 genomes dataset as input.

Download or read book Robust and Privacy Preserving Federated Learning written by Fatima Elhattab and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's rapidly evolving digital landscape, machine learning has become an in- dispensable and transformative force, as substantiated by extensive research studies. Its profound impact spans across diverse industries, offering ground breaking solutions and innovations that have reshaped the way we interact with technology and make decisions. From recommendation systems enhancing content delivery on platforms to the presence of virtual personal assistants like Siri and Alexa, capable of understanding and responding to natural language commands, the applications of machine learning are both diverse and impactful. In domains like healthcare, it aids in disease diagnosis, while in finance, it fortifies fraud detection and risk assessment. This ubiquity of machine learning signifies not just a technological trend but a fundamental shift in problem-solving and decision-making approaches. However, this surge in data-driven innovation has raised a paramount concern - the protection of individuals' privacy and personal data. The General Data Protection Regulation (GDPR) exemplifies the heightened importance of data privacy in our modern era. As machine learning becomes increasingly intertwined with our daily lives, achieving a delicate balance between technological advancements and safeguarding individual privacy has become imperative. Moreover, addressing these concerns has given rise to the concept of privacy-preserving machine learning, with federated learning emerging as a pivotal technique, redefining collaborative machine learning by enabling multiple parties to build a shared model without sharing their raw data. Federated Learning represents a promising paradigm in Machine Learning, enabling collaborative model training among decentralized devices in edge computing systems. However, it exhibits susceptibility to various attacks. This research is divided into two main thrusts, each addressing critical security and privacy challenges in the context of Federated Learning. The first thrust focuses on countering poisoning attacks for robust Federated Learning, where adversaries aim to introduce harmful tasks into federated models alongside their main tasks. To detect these attacks, the research introduces ARMOR, a novel GAN-based attack detection system that analyzes the information embedded in model updates. The second thrust deals with countering inference attacks for privacy-preserving Federated Learning, specifically membership inference attacks. To bolster privacy in FL, two novel approaches are introduced: PASTEL, which enhances FL systems' resilience against MIAs by minimizing the internal generalization gap, and DINAR, a fine-grained privacy-preserving FL method that obfuscates privacy-sensitive layers and employs adaptive gradient descent to enhance model utility. These research objectives collectively aim to address security and privacy challenges and advance the field of federated learning.

Download or read book Towards Ethical and Robust Privacy preserving Machine Learning written by Hui Hu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Privacy in machine learning has received tremendous attention in recent years, which mainly involves data privacy and model privacy. Recent studies have revealed numerous privacy attacks and privacy-preserving methodologies, that vary across a broad range of applications. To date, however, there exist few powerful methodologies in addressing privacy-preserving challenges in ethical machine learning and deep learning due to the difficulty of guaranteeing model robustness and privacy-preserving simultaneously. In this dissertation, two critical problems will be investigated and addressed: data privacy-preserving in ethical machine learning, and model privacy-preserving in deep learning under powerful side-channel power attacks. First, we investigate the problem of data privacy-preserving in ethical machine learning with the following two considerations: (1) Users’ privacy (i.e., race, religion, gender, etc.) is severely leaked in ethical machine learning as most existing techniques require full access to sensitive personal data to achieve model fairness. To address this pressing privacy issue, we propose a distributed privacy-preserving fair machine learning mechanism based on random projection theory and multi-party computation. Through rigorous theoretical analysis and comprehensive simulations, we can prove that the proposed mechanism is efficient for privacy-preserving while guaranteeing good model robustness. Further, (2) considering the dependency relation of graph data in ethical machine learning, an individual’s privacy can be leaked due to the sensitive information disclosure of their neighbors. Typically, in a graph neural network, the sensitive information disclosure of non-private users potentially exposes the sensitive information of private users in the same graph owing to the homophily property and message-passing mechanism of graph neural networks. To address this problem, based on disentangled representation learning, we propose a principled privacy-preserving graph neural network model to mitigate individual privacy leakage of private users in a graph, which maintains competitive model accuracy compared with non-private graph neural networks. We verify the effectiveness of the proposed privacy-preserving model through extensive experiments and theoretical analysis. Second, as the disclosure of model privacy can allow adversaries to potentially infer users’ extremely sensitive decisions, further, we study model privacy-preserving in deep learning under side-channel power attacks. Side-channel power attacks are powerful attacks that infer the internal information of a traditional deep neural network (i.e., model privacy), which can be leveraged to infer some important decisions of users. Therefore, with the increasing applications of deep learning, training privacy-preserving deep neural networks under side-channel power attacks is a pressing task. This dissertation proposes an efficient solution for training privacy-preserving deep neural networks to resist powerful side-channel power attacks, which randomly trains multiple independent sub-networks to generate random power traces in the temporal domain. The comprehensive theoretical analysis and experimental results demonstrate the effectiveness of the proposed approach in model privacy-preserving and model robustness under side-channel power attacks.

Download or read book Privacy preserving Cloud assisted Data Analytics written by Wei Bao and published by . This book was released on 2021 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users' data may contain private information that needs to be protected. Cloud computing has become more and more popular in both academia and industry communities. By pooling infrastructure and servers together, it can offer virtually unlimited resources easily accessible via the Internet. Various services could be provided by cloud platforms including machine learning and data analytics. The goal of this dissertation is to develop privacy-preserving cloud-assisted data analytics solutions to address the aforementioned challenges, leveraging the powerful and easy-to-access cloud. In particular, we propose the following systems. To address the problem of limited computation power at user and the need of privacy protection in data analytics, we consider geometric programming (GP) in data analytics, and design a secure, efficient, and verifiable outsourcing protocol for GP. Our protocol consists of a transform scheme that converts GP to DGP, a transform scheme with computationally indistinguishability, and an efficient scheme to solve the transformed DGP at the cloud side with result verification. Evaluation results show that the proposed secure outsourcing protocol can achieve significant time savings for users. To address the problem of limited data at individual users, we propose two distributed learning systems such that users can collaboratively train machine learning models without losing privacy. The first one is a differentially private framework to train logistic regression models with distributed data sources. We employ the relevance between input data features and the model output to significantly improve the learning accuracy. Moreover, we adopt an evaluation data set at the cloud side to suppress low-quality data sources and propose a differentially private mechanism to protect user's data quality privacy. Experimental results show that the proposed framework can achieve high utility with low quality data, and strong privacy guarantee. The second one is an efficient privacy-preserving federated learning system that enables multiple edge users to collaboratively train their models without revealing dataset. To reduce the communication overhead, we select well-aligned and large-enough magnitude gradients for uploading which leads to quick convergence. To minimize the noise added and improve model utility, each user only adds a small amount of noise to his selected gradients, encrypts the noise gradients before uploading, and the cloud server will only get the aggregate gradients that contain enough noise to achieve differential privacy. Evaluation results show that the proposed system can achieve high accuracy, low communication overhead, and strong privacy guarantee. In future work, we plan to design a privacy-preserving data analytics with fair exchange, which ensures the payment fairness. We will also consider designing distributed learning systems with heterogeneous architectures.

Download or read book Federated Learning written by Qiang Yang and published by Springer Nature. This book was released on 2020-11-25 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”

Download or read book Advances and Open Problems in Federated Learning written by Peter Kairouz and published by . This book was released on 2021-06-23 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.Since then, the topic has gathered much interest across many different disciplines and the realization that solving many of these interdisciplinary problems likely requires not just machine learning but techniques from distributed optimization, cryptography, security, differential privacy, fairness, compressed sensing, systems, information theory, statistics, and more.This monograph has contributions from leading experts across the disciplines, who describe the latest state-of-the art from their perspective. These contributions have been carefully curated into a comprehensive treatment that enables the reader to understand the work that has been done and get pointers to where effort is required to solve many of the problems before Federated Learning can become a reality in practical systems.Researchers working in the area of distributed systems will find this monograph an enlightening read that may inspire them to work on the many challenging issues that are outlined. This monograph will get the reader up to speed quickly and easily on what is likely to become an increasingly important topic: Federated Learning.

Download or read book The EU General Data Protection Regulation GDPR written by Paul Voigt and published by Springer. This book was released on 2017-08-07 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides expert advice on the practical implementation of the European Union’s General Data Protection Regulation (GDPR) and systematically analyses its various provisions. Examples, tables, a checklist etc. showcase the practical consequences of the new legislation. The handbook examines the GDPR’s scope of application, the organizational and material requirements for data protection, the rights of data subjects, the role of the Supervisory Authorities, enforcement and fines under the GDPR, and national particularities. In addition, it supplies a brief outlook on the legal consequences for seminal data processing areas, such as Cloud Computing, Big Data and the Internet of Things.Adopted in 2016, the General Data Protection Regulation will come into force in May 2018. It provides for numerous new and intensified data protection obligations, as well as a significant increase in fines (up to 20 million euros). As a result, not only companies located within the European Union will have to change their approach to data security; due to the GDPR’s broad, transnational scope of application, it will affect numerous companies worldwide.